In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)
In [2]:
## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.13.0.post200
1.9.1
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model¶

In [6]:
model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"best_ALL_1130_continue.pth", map_location=torch.device('cpu')))
model = model
model.eval()
Out[6]:
ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)
In [7]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [8]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000
In [ ]:
## GRN for the transition t1 to t3
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 1).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 3).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm
In [ ]:
# Save the array to disk
np.save('genesys_ctw_t1-t3.npy', ctw)
In [9]:
ctw = np.load('genesys_ctw_t1-t3.npy')
In [10]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [11]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [12]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [13]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [14]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[14]:
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: Name, dtype: int64

Network analysis¶

In [15]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [23]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [24]:
atri = network(0)
In [25]:
tri = network(1)
In [26]:
cor = network(2)
In [27]:
end = network(3)
In [28]:
per = network(4)
In [29]:
pro = network(5)
In [30]:
xyl = network(6)
In [31]:
phl = network(7)
In [32]:
lrc = network(8)
In [33]:
col = network(9)
In [34]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [35]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts(), columns=['tf_occurance'])
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [36]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[36]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
ATS 0.911741 0.802023 0.768044 0.091463 0.184991 0.119537 0.585366 0.659686 0.470437 9 13.593288
GL2 0.940337 0.581637 0.794062 0.095528 0.179756 0.255784 0.510163 0.073298 0.105398 9 12.535963
WRKY9 0.263193 0.154999 0.983636 0.193089 0.467714 0.223650 0.497967 0.256545 0.425450 9 12.466244
TTG2 0.905573 0.391550 0.014513 0.193089 0.094241 0.127249 0.422764 0.055846 0.068123 9 11.272949
GATA17 0.817301 0.000162 0.316965 0.237805 0.073298 0.332905 0.223577 0.017452 0.132391 9 11.151855
TRY 0.727121 0.008339 0.371758 0.199187 0.109948 0.136247 0.290650 0.020942 0.023136 9 10.887328
FIT 0.811816 0.000531 0.087486 0.099593 0.097731 0.119537 0.422764 0.118674 0.118252 9 10.876385
CRF4 0.705881 0.068896 0.013112 0.134146 0.059337 0.124679 0.315041 0.080279 0.086118 9 10.587488
WRKY72 0.330754 0.293429 0.188152 0.197154 0.040140 0.053985 0.034553 0.078534 0.071979 9 10.288680
OFP18 0.674254 0.000802 0.000374 0.101626 0.017452 0.033419 0.223577 0.015707 0.006427 9 10.073639
WRKY11 0.321796 0.000485 0.125334 0.150407 0.022688 0.221080 0.087398 0.022688 0.110540 9 10.062415
OFP15 0.326060 0.003091 0.144207 0.063008 0.066318 0.055270 0.085366 0.019197 0.083548 9 9.846064
AT2G36026 0.232900 0.009815 0.126164 0.052846 0.095986 0.014139 0.123984 0.015707 0.136247 9 9.807787
AT3G14740 0.154741 0.033711 0.025035 0.134146 0.085515 0.093830 0.111789 0.040140 0.038560 9 9.717467
In [37]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[37]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
AT3G53370 0.879734 0.918494 0.085366 0.321117 0.060976 0.820244 6 9.085931
ATMYC1 0.509273 0.747507 0.695122 0.218150 0.028455 0.549738 6 8.748245
RSL1 0.178295 0.487674 0.640244 0.287958 0.012195 0.179756 6 7.786121
IAA14 0.886464 0.000366 0.158537 0.151832 0.207317 0.308901 6 7.713417
EGL3 0.064229 0.406183 0.729675 0.202443 0.022358 0.157068 6 7.581956
RHD6 0.262365 0.557668 0.205285 0.139616 0.020325 0.350785 6 7.536044
WRI1 0.754682 0.001751 0.260163 0.146597 0.164634 0.010471 6 7.338298
In [38]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[38]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
AT1G27050 0.006512 0.174797 0.046748 3 3.228056
NAC044 0.000886 0.152439 0.020325 3 3.173650
KAN2 0.000604 0.140244 0.004065 3 3.144913
In [39]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
WRKY75 0.946683 0.006981 0.083770 3 4.037433
AT4G09100 0.846737 0.031414 0.043630 3 3.921780
MED6 0.401918 0.005236 0.090750 3 3.497904
RSL4 0.186181 0.017452 0.115183 3 3.318816
PRMT3 0.055062 0.001745 0.001745 3 3.058553
LRL3 0.038312 0.001745 0.031414 3 3.071471
AT1G02040 0.016997 0.029668 0.043630 3 3.090296
AT4G39160 0.008973 0.024433 0.116928 3 3.150334
MYB47 0.004546 0.097731 0.041885 3 3.144162
WRKY61 0.001303 0.008726 0.008726 3 3.018755
RL6 0.000433 0.047120 0.061082 3 3.108636
AT1G11490 0.000296 0.041885 0.034904 3 3.077085
AT2G17600 0.000146 0.054101 0.033159 3 3.087406
AT2G32030 0.000012 0.050611 0.017452 3 3.068075
WRKY35 0.000009 0.066318 0.059337 3 3.125664
In [40]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[40]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
GRF2 0.492789 0.109254 0.084833 3 3.686877
MIF3 0.356946 0.024422 0.037275 3 3.418643
AT5G16680 0.298771 0.007712 0.014139 3 3.320622
AGL94 0.152410 0.029563 0.055270 3 3.237243
CZF1 0.138546 0.029563 0.116967 3 3.285076
CRF8 0.132505 0.030848 0.055270 3 3.218623
HDG2 0.106891 0.098972 0.035990 3 3.241852
AT4G39780 0.104858 0.021851 0.025707 3 3.152415
OFP6 0.094998 0.008997 0.146530 3 3.250525
TCX2 0.083117 0.106684 0.055270 3 3.245071
AT3G19360 0.046808 0.111825 0.023136 3 3.181770
AT3G02890 0.045533 0.041131 0.078406 3 3.165070
WRKY7 0.037333 0.146530 0.034704 3 3.218567
HDG7 0.025543 0.061697 0.030848 3 3.118088
TGA7 0.023758 0.023136 0.024422 3 3.071316
AGL26 0.009873 0.008997 0.011568 3 3.030438
NLP7 0.008526 0.010283 0.012853 3 3.031662
CSDP1 0.006225 0.133676 0.089974 3 3.229875
AT2G41710 0.003628 0.024422 0.042416 3 3.070466
HDG1 0.003474 0.079692 0.059126 3 3.142291
WRKY26 0.001899 0.019280 0.008997 3 3.030177
SR1 0.001689 0.033419 0.047558 3 3.082666
TFIIIA 0.001229 0.034704 0.074550 3 3.110484
PYE 0.000734 0.047558 0.024422 3 3.072714
AT1G19000 0.000657 0.025707 0.019280 3 3.045644
CRF11 0.000278 0.055270 0.064267 3 3.119815
NAC053 0.000069 0.010283 0.016710 3 3.027062
SNL6 0.000066 0.037275 0.060411 3 3.097753
CHR1 0.000041 0.086118 0.044987 3 3.131147
LDL1 0.000015 0.048843 0.024422 3 3.073280
GATA6 0.000002 0.132391 0.037275 3 3.169667
In [41]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[41]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
KNAT3 0.366312 0.063197 0.061958 3 3.491467
FBH4 0.338893 0.016109 0.254027 3 3.609029
WRKY33 0.233397 0.078067 0.104089 3 3.415553
NTT 0.004625 0.022305 0.021066 3 3.047995
AT5G41020 0.002829 0.040892 0.037175 3 3.080896
AT5G06110 0.001770 0.058240 0.057001 3 3.117011
RAP2.1 0.001268 0.008674 0.019827 3 3.029769
CHR4 0.001248 0.076828 0.063197 3 3.141273
MYB73 0.001239 0.003717 0.047088 3 3.052045
ARF8 0.001238 0.014870 0.052045 3 3.068152
AT2G20110 0.001216 0.013631 0.009913 3 3.024760
CDC5 0.000086 0.114002 0.035936 3 3.150024
TOE3 0.000051 0.042131 0.013631 3 3.055813
AT5G65910 0.000037 0.018587 0.028501 3 3.047125
RDUF1 0.000020 0.033457 0.133829 3 3.167306
JMJ18 0.000003 0.017348 0.042131 3 3.059483
In [42]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[42]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
ARR3 0.013035 0.030588 0.336049 0.306773 0.490835 0.864542 6 8.041822
MYB36 0.056428 0.013912 0.360489 0.400398 0.048880 1.001992 6 7.882099
MYB3 0.009053 0.020278 0.378819 0.372510 0.095723 0.992032 6 7.868414
JKD 0.015333 0.014429 0.354379 0.348606 0.596741 0.444223 6 7.773711
MYB68 0.000233 0.012409 0.252546 0.308765 0.024440 0.974104 6 7.572496
... ... ... ... ... ... ... ... ...
AT4G25610 0.000254 0.000004 0.014257 0.041833 0.010183 0.009960 6 6.076490
RLT1 0.000154 0.000159 0.016293 0.035857 0.010183 0.005976 6 6.068622
AT3G12130 0.000017 0.000231 0.014257 0.041833 0.002037 0.003984 6 6.062357
AT3G61420 0.000175 0.000080 0.002037 0.011952 0.008147 0.031873 6 6.054262
PTM 0.000050 0.000191 0.006110 0.031873 0.008147 0.003984 6 6.050354

72 rows × 8 columns

In [43]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
SIGF 0.010765 0.004073 0.010183 3 3.025022
WRKY57 0.004181 0.004073 0.069246 3 3.077501
HAM2 0.002789 0.036660 0.054990 3 3.094439
MYB14 0.002523 0.026477 0.134420 3 3.163419
AT2G42660 0.002415 0.048880 0.232179 3 3.283474
EIL1 0.002365 0.016293 0.109980 3 3.128638
TAFII59 0.002107 0.030550 0.006110 3 3.038767
HMGB2 0.001866 0.024440 0.024440 3 3.050746
HB16 0.001833 0.014257 0.091650 3 3.107739
RR2 0.001397 0.002037 0.006110 3 3.009543
TAFII21 0.001343 0.018330 0.006110 3 3.025782
HMG 0.001305 0.038697 0.065173 3 3.105175
AT1G68070 0.001288 0.030550 0.034623 3 3.066462
SIGE 0.001218 0.044807 0.146640 3 3.192664
AT5G63700 0.001143 0.022403 0.158859 3 3.182406
NAC78 0.000960 0.004073 0.016293 3 3.021327
GLK2 0.000786 0.004073 0.028513 3 3.033372
LHW 0.000711 0.061100 0.006110 3 3.067921
BPC6 0.000678 0.008147 0.004073 3 3.012897
NAC003 0.000333 0.010183 0.073320 3 3.083836
AT4G28030 0.000320 0.004073 0.079430 3 3.083823
NFXL2 0.000254 0.020367 0.022403 3 3.043023
RGL1 0.000224 0.008147 0.002037 3 3.010408
BNQ3 0.000175 0.004073 0.006110 3 3.010358
BAM7 0.000162 0.014257 0.010183 3 3.024602
AT1G67310 0.000145 0.010183 0.010183 3 3.020512
ZFN3 0.000133 0.014257 0.032587 3 3.046976
RTV1 0.000116 0.081466 0.028513 3 3.110096
EPR1 0.000096 0.028513 0.030550 3 3.059159
TSO1 0.000083 0.050916 0.020367 3 3.071366
BEH4 0.000050 0.022403 0.059063 3 3.081516
MYC4 0.000037 0.010183 0.020367 3 3.030587
B160 0.000037 0.018330 0.012220 3 3.030587
AT1G04850 0.000021 0.012220 0.030550 3 3.042791
ATMAK3 0.000017 0.010183 0.002037 3 3.012237
AT5G47390 0.000017 0.061100 0.042770 3 3.103886
AT5G28300 0.000012 0.022403 0.038697 3 3.061112
HAM1 0.000008 0.014257 0.006110 3 3.020375
JMJ14 0.000008 0.012220 0.006110 3 3.018338
AT2G43280 0.000008 0.018330 0.006110 3 3.024448
bHLH34 0.000008 0.014257 0.008147 3 3.022412
AT3G18960 0.000008 0.046843 0.016293 3 3.063145
RHC2A 0.000008 0.012220 0.016293 3 3.028522
DBP1 0.000008 0.016293 0.034623 3 3.050925
SUVR5 0.000004 0.006110 0.006110 3 3.012224
AT5G12440 0.000004 0.052953 0.006110 3 3.059067
In [44]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
chr31 0.026851 0.025896 0.225100 3 3.277847
AT2G27930 0.010986 0.023904 0.107570 3 3.142460
NAC083 0.009085 0.031873 0.179283 3 3.220241
AT2G45120 0.008867 0.009960 0.061753 3 3.080580
IAA16 0.007960 0.021912 0.085657 3 3.115530
... ... ... ... ... ...
AT5G26749 0.000004 0.013944 0.007968 3 3.021916
SPL7 0.000004 0.025896 0.007968 3 3.033869
CIP8 0.000004 0.033865 0.009960 3 3.043829
NF-YA1 0.000004 0.011952 0.003984 3 3.015940
SDG20 0.000004 0.015936 0.003984 3 3.019924

92 rows × 5 columns

In [45]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[45]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
AT3G43430 0.148291 0.322640 0.075969 0.056666 0.521499 0.415318 0.246032 0.515436 0.797133 0.996764 0.219577 0.860403 12 17.175729
HB-8 0.536432 0.109958 0.712503 0.021305 0.044101 0.190939 0.500000 0.629530 0.027563 0.519957 0.944444 0.014765 12 16.251499
MYB20 0.100236 0.053263 0.001600 0.030719 0.489526 0.606257 0.137566 0.633557 0.414553 0.838188 0.417989 0.025503 12 15.748958
MYB43 0.182036 0.004801 0.000372 0.041344 0.272326 0.507012 0.116402 0.657718 0.243660 0.549083 0.023810 0.034899 12 14.633464
AT1G61660 0.111503 0.010047 0.000379 0.028837 0.004410 0.388350 0.050265 0.422819 0.197354 0.817691 0.126984 0.108725 12 14.267364
AT1G12440 0.001649 0.205924 0.315173 0.001460 0.060639 0.426106 0.261905 0.259060 0.027563 0.139159 0.124339 0.063087 12 13.886064
TCP14 0.002147 0.000037 0.002028 0.162237 0.033076 0.132686 0.370370 0.398658 0.016538 0.333333 0.206349 0.175839 12 13.833299
In [46]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[46]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
LBD14 0.626412 0.192944 0.170893 3 3.990249
AT3G21330 0.354888 0.477398 0.058434 3 3.890720
SAP 0.253036 0.636163 0.020948 3 3.910147
AT1G27660 0.062708 0.153252 0.146637 3 3.362598
GATA23 0.042362 0.119074 0.062845 3 3.224281
AT3G60580 0.040281 0.130099 0.037486 3 3.207867
ATWHY2 0.035955 0.051819 0.094818 3 3.182593
NAC001 0.033811 0.060639 0.151047 3 3.245498
bZIP5 0.031200 0.101433 0.183021 3 3.315654
WHY1 0.018266 0.132304 0.008820 3 3.159391
LBD16 0.015147 0.208379 0.366042 3 3.589568
AT5G13780 0.013032 0.027563 0.039691 3 3.080287
GRF9 0.008439 0.023153 0.006615 3 3.038208
IDD11 0.005733 0.039691 0.116869 3 3.162293
WHY3 0.001090 0.009923 0.013230 3 3.024244
AT2G20100 0.001059 0.050717 0.122381 3 3.174157
AT1G63820 0.000712 0.047409 0.058434 3 3.106555
TRFL10 0.000692 0.048512 0.049614 3 3.098818
TEM1 0.000286 0.015436 0.091510 3 3.107232
SAC51 0.000002 0.018743 0.067255 3 3.086000
NF-YC9 0.000002 0.098126 0.040794 3 3.138922
In [47]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[47]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
AT5G60142 0.251327 0.011866 0.003236 3 3.266430
AT4G20970 0.053128 0.021575 0.001079 3 3.075782
HAT3 0.029663 0.004315 0.042071 3 3.076049
SHY2 0.016662 0.011866 0.074434 3 3.102962
AT1G32810 0.000185 0.007551 0.002157 3 3.009894
ABF4 0.000104 0.011866 0.009709 3 3.021679
MAF1 0.000087 0.038835 0.009709 3 3.048631
AT-HSFA5 0.000059 0.022654 0.002157 3 3.024871
AT1G10720 0.000040 0.024811 0.010787 3 3.035638
SNL1 0.000037 0.017260 0.009709 3 3.027006
TOC1 0.000037 0.006472 0.009709 3 3.016219
BZO2H1 0.000037 0.020496 0.009709 3 3.030242
AT3G11450 0.000037 0.030205 0.009709 3 3.039951
AT3G62240 0.000029 0.015102 0.009709 3 3.024840
CHB3 0.000029 0.026969 0.009709 3 3.036707
NPH4 0.000029 0.032362 0.009709 3 3.042100
MAF5 0.000028 0.021575 0.009709 3 3.031312
FES1 0.000026 0.006472 0.002157 3 3.008656
AIF1 0.000019 0.025890 0.050701 3 3.076610
AT1G69570 0.000014 0.010787 0.034520 3 3.045321
AT2G18090 0.000014 0.057174 0.028047 3 3.085235
OFP1 0.000014 0.038835 0.030205 3 3.069054
TGA6 0.000014 0.009709 0.002157 3 3.011880
AT2G19260 0.000013 0.009709 0.003236 3 3.012958
HB-1 0.000012 0.038835 0.047465 3 3.086312
HSFA1D 0.000009 0.009709 0.002157 3 3.011876
STH 0.000009 0.022654 0.030205 3 3.052868
TGA4 0.000008 0.009709 0.008630 3 3.018347
AT2G45460 0.000007 0.010787 0.030205 3 3.040999
AT2G03470 0.000007 0.026969 0.025890 3 3.052866
CRF12 0.000006 0.038835 0.022654 3 3.061494
ATU2AF35A 0.000006 0.052859 0.009709 3 3.062573
AT1G75490 0.000005 0.006472 0.080906 3 3.087383
WRKY4 0.000005 0.009709 0.019417 3 3.029131
NTL9 0.000005 0.030205 0.030205 3 3.060415
WRI4 0.000005 0.009709 0.030205 3 3.039918
TBP1 0.000005 0.011866 0.007551 3 3.019422
NAGS1 0.000003 0.030205 0.009709 3 3.039917
VIP1 0.000003 0.045307 0.009709 3 3.055020
AT2G23780 0.000003 0.039914 0.010787 3 3.050705
LBD37 0.000002 0.016181 0.058252 3 3.074436
MBD5 0.000002 0.030205 0.004315 3 3.034522
AT2G06025 0.000002 0.018339 0.009709 3 3.028050
AT1G01640 0.000001 0.161812 0.038835 3 3.200648
AT5G40710 0.000001 0.010787 0.008630 3 3.019419
TRB1 0.000001 0.031284 0.010787 3 3.042072
IDD16 0.000001 0.008630 0.020496 3 3.029127
AT4G31420 0.000001 0.045307 0.009709 3 3.055017
AT1G32360 0.000001 0.011866 0.008630 3 3.020497
HB21 0.000001 0.049622 0.099245 3 3.148868
PLIM2b 0.000001 0.008630 0.023732 3 3.032364
ELF6 0.000001 0.009709 0.003236 3 3.012946
In [48]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[48]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
AT1G68810 0.932915 0.558201 0.960317 3 5.451434
VND2 0.889296 0.169312 0.912698 3 4.971306
HB31 0.888622 0.206349 0.087302 3 4.182273
MYB46 0.784255 0.333333 0.462963 3 4.580551
VND7 0.781714 0.095238 0.576720 3 4.453672
XND1 0.671852 0.251323 0.867725 3 4.790900
HAT14 0.628465 0.156085 0.857143 3 4.641692
ETC1 0.616276 0.079365 0.576720 3 4.272360
AGL58 0.572243 0.029101 0.177249 3 3.778592
IAA6 0.571646 0.272487 0.857143 3 4.701276
AT1G68200 0.521859 0.066138 0.235450 3 3.823446
VND1 0.497776 0.544974 0.656085 3 4.698834
JLO 0.442950 0.357143 0.084656 3 3.884749
ASL9 0.432052 0.298942 0.119048 3 3.850041
VND5 0.361760 0.105820 0.542328 3 4.009908
VND4 0.289447 0.076720 0.589947 3 3.956114
AT5G03510 0.277792 0.280423 0.113757 3 3.671972
BZIP49 0.270269 0.142857 0.042328 3 3.455455
MYB83 0.256410 0.042328 0.423280 3 3.722019
IAA31 0.242404 0.335979 0.894180 3 4.472563
MYB25 0.183122 0.039683 0.044974 3 3.267778
NAC075 0.178912 0.269841 0.611111 3 4.059864
VND3 0.164526 0.047619 0.904762 3 4.116907
MYB52 0.041212 0.335979 0.026455 3 3.403646
NAC010 0.030637 0.396825 0.063492 3 3.490955
KNAT7 0.015775 0.261905 0.198413 3 3.476092
TLP2 0.014498 0.171958 0.164021 3 3.350476
MYB50 0.010568 0.087302 0.251323 3 3.349192
TCP20 0.010568 0.066138 0.330688 3 3.407393
BLH3 0.007494 0.113757 0.018519 3 3.139770
AP3 0.006245 0.164021 0.814815 3 3.985081
NST1 0.005698 0.058201 0.121693 3 3.185592
VND6 0.005284 0.002646 0.066138 3 3.074067
AT4G00940 0.005284 0.047619 0.103175 3 3.156078
AT1G26610 0.005277 0.063492 0.187831 3 3.256600
MYB99 0.004954 0.103175 0.137566 3 3.245695
AT5G18090 0.003523 0.007937 0.034392 3 3.045851
TCP10 0.002646 0.148148 0.198413 3 3.349206
LBD31 0.002077 0.343915 0.021164 3 3.367156
BEL10 0.000035 0.097884 0.007937 3 3.105855
DOF1 0.000021 0.156085 0.460317 3 3.616423
In [49]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[49]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
DAR2 0.757107 0.132886 0.855034 3 4.745026
NAC020 0.707076 0.048322 0.535570 3 4.290968
HCA2 0.668388 0.055034 0.657718 3 4.381139
AT2G03500 0.667116 0.079195 0.895302 3 4.641613
REM22 0.576102 0.110067 0.120805 3 3.806975
GATA20 0.502163 0.028188 0.280537 3 3.810888
NAC057 0.064336 0.102013 0.732886 3 3.899235
AT3G12730 0.036159 0.083221 0.906040 3 4.025420
AT1G26790 0.006888 0.069799 0.083221 3 3.159908
BRH1 0.001967 0.068456 0.139597 3 3.210020
AT5G41380 0.001687 0.093960 0.394631 3 3.490277
NAC2 0.001310 0.099329 0.251007 3 3.351645
RMA2 0.000832 0.175839 0.292617 3 3.469288
CDF3 0.000765 0.079195 0.185235 3 3.265194
VOZ1 0.000687 0.102013 0.208054 3 3.310754
DOF2.4 0.000233 0.103356 0.468456 3 3.572045
DOF6 0.000148 0.144966 0.908725 3 4.053839

Search for individual genes¶

In [50]:
gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[50]:
tf_occurance end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality per_degree_centrality per_out_centrality per_in_centrality ... pro_out_centrality pro_in_centrality pro_closeness_centrality pro_eigenvector_centrality xyl_degree_centrality xyl_out_centrality xyl_in_centrality xyl_betweenness_centrality xyl_closeness_centrality xyl_eigenvector_centrality
SHR 3.0 0.179283 0.151394 0.027888 0.000879 0.000501 0.056736 0.367144 0.24366 0.123484 ... 0.100324 0.021575 0.000384 0.046686 0.174603 0.058201 0.116402 0.04416 0.000484 0.04425

1 rows × 24 columns

In [51]:
gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[51]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality
BLJ 2.0 0.130346 0.095723 0.034623 0.000021 0.000296 0.055857 0.920319 0.135458 0.784861 0.01771 0.000491 0.127557
In [52]:
gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[52]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
JKD 2.0 0.95112 0.354379 0.596741 0.015333 0.000348 0.143791 0.792829 0.348606 0.444223 0.014429 0.000572 0.110048 0.034696 0.016109 0.018587 0.00029 0.019235
In [53]:
gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[53]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
RVN 2.0 0.181263 0.11609 0.065173 0.000004 0.000305 0.068971 0.272908 0.189243 0.083665 0.000095 0.000523 0.07088 0.006196 0.004957 0.001239 0.000201 0.005268
In [54]:
gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[54]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality
BIB 2.0 0.071283 0.065173 0.00611 0.000012 0.000276 0.040438 0.436255 0.099602 0.336653 0.002239 0.000459 0.089354
In [55]:
gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[55]:
In [56]:
gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[56]:
In [57]:
gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[57]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality tri_betweenness_centrality tri_closeness_centrality tri_eigenvector_centrality lrc_degree_centrality lrc_out_centrality lrc_in_centrality lrc_betweenness_centrality lrc_closeness_centrality lrc_eigenvector_centrality
GL2 3.0 0.605691 0.095528 0.510163 0.940337 0.001224 0.109614 0.253054 0.179756 0.073298 0.581637 0.000692 0.083733 0.361183 0.255784 0.105398 0.794062 0.000822 0.086665
In [58]:
tf_spec.to_csv('TF_GRN_centrality_t1-t3_zscore3.csv', index=True)
In [ ]: